Hi, I'm Mike

My research spans statistical machine learning and its applications in healthcare and the sciences.

I am currently a postdoctoral fellow in computer science at Harvard SEAS, advised by
Prof. Finale Doshi-Velez.
I completed my Ph.D. in computer science at Brown University in May 2016, advised by Prof. Erik Sudderth.

Job Search!

I am looking for tenure-track faculty positions (2017-18). Please reach out if you have questions.

can we find clusters of cooccuring words that thematically organize every New York Times article from the last 20 years?

can we find clusters of cooccuring epigenetic modifiers that amplify or inhibit gene expression?

To answer these questions, we developed new variational inference
algorithms for a broad family of Bayesian nonparametric models that include mixtures, topic models, sequential models, and relational models.
Our key innovations include scaling to millions of examples and adding data-driven split/merge proposal moves to avoid poor local minima.

News

[Jan 2018] Paper accepted to AISTATS 2018.

Our paper -- Semi-Supervised Prediction-Constrained Topic Models -- describes a new framework for training topic models and other latent variable models to improve supervised predictions while still providing good generative models with interpretable topics. The new approach fixes core issues with past methods like sLDA, and shines especially in semi-supervised tasks, when only a small fraction of training examples are labeled.

[Aug 2016] Started post-doc at Harvard

[May 2016] Successful Ph.D. defense!

Many thanks to family and friends who supported me along the way.

[Jan 2016] Invited talks on my thesis.

I visited several research groups at Northeastern, U. Washington, and MIT to
discuss results from my thesis work trying to make
effective variational inference for clustering that scales to millions of examples.
[slides PDF]
[slides PPTX]